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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Chapter 18: Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

energy distribution↗
DOE OSTI2015

Exploring Two Approaches for an End-to-End Scientific Analysis Workflow

The advance of the scientific discovery process is accomplished by the integration of independently-developed programs run on disparate computing facilities into coherent workflows usable by scientists who are not experts in computing. For such advancement, we need a system which scientists can use to formulate analysis workflows, to integrate new components to these workflows, and to execute different components on resources that are best suited to run those components. In addition, we need to monitor the status of the workflow as components get scheduled and executed, and to access the intermediate and final output for visual exploration and analysis. Finally, it is important for scientists to be able to share their workflows with collaborators. Moreover we have explored two approaches for such an analysis framework for the Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC), the first one is based on the use and extension of Galaxy, a web-based portal for biomedical research, and the second one is based on a programming language, Python. In our paper, we present a brief description of the two approaches, describe the kinds of extensions to the Galaxy system we have found necessary in order to support the wide variety of scientific analysis in the cosmology community, and discuss how similar efforts might be of benefit to the HEP community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Comparative Genomics Using the Integrated Microbial Genomes and Microbiomes (IMG/M) System: A Deinococcus Use Case

The Integrated Microbial Genomes and Microbiomes (IMG/M) system is a web-based platform that provides access to the wealth of public sequence data arising from diverse environments and enables the user to answer biological questions. In this review, we explore IMG’s tools and features using genome data for genus Deinococcus isolates as well as metagenome-assembled genomes (MAGs). Here, we use various comparative genomic and visualization tools to investigate this genus and address specific research questions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

DOE SciDAC’s Earth System Grid Center for Enabling Technologies Final Report for University of Southern California Information Sciences Institute

The mission of the Earth System Grid Federation (ESGF) is to provide the worldwide climate-research community with access to the data, information, model codes, analysis tools, and intercomparison capabilities required to make sense of enormous climate data sets. Its specific goals are to (1) provide an easy-to-use and secure web-based data access environment for data sets; (2) add value to individual data sets by presenting them in the context of other data sets and tools for comparative analysis; (3) address the specific requirements of participating organizations with respect to bandwidth, access restrictions, and replication; (4) ensure that the data are readily accessible through the analysis and visualization tools used by the climate research community; and (5) transfer infrastructure advances to other domain areas. For the ESGF, the U.S. Department of Energy’s (DOE’s) Earth System Grid Center for Enabling Technologies (ESG-CET) team has led international development and delivered a production environment for managing and accessing ultra-scale climate data. This production environment includes multiple national and international climate projects (such as the Community Earth System Model and the Coupled Model Intercomparison Project), ocean model data (such as the Parallel Ocean Program), observation data (Atmospheric Radiation Measurement Best Estimate, Carbon Dioxide Information and Analysis Center, Atmospheric Infrared Sounder, etc.), and analysis and visualization tools, all serving a diverse user community. These data holdings and services are distributed across multiple ESG-CET sites (such as ANL, LANL, LBNL/NERSC, LLNL/PCMDI, NCAR, and ORNL) and at unfunded partner sites, such as the Australian National University National Computational Infrastructure, the British Atmospheric Data Centre, the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Max Planck Institute for Meteorology, the German Climate Computing Centre, the National Aeronautics and Space Administration Jet Propulsion Laboratory, and the National Oceanic and Atmospheric Administration. The ESGF software is distinguished from other collaborative knowledge systems in the climate community by its widespread adoption, federation capabilities, and broad developer base. It is the leading source for present climate data holdings, including the most important and largest data sets in the globalclimate community, and—assuming its development continues—we expect it to be the leading source for future climate data holdings as well. Recently, ESG-CET extended its services beyond data-file access and delivery to include more detailed information products (scientific graphics, animations, etc.), secure binary data-access services (based upon the OPeNDAP Data Access Protocol), and server-side analysis. The latter capabilities allow users to request data subsets transformed through commonly used analysis and intercomparison procedures. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users seeking to understand, process, extract value from, visualize, and/or communicate it to others—this is of course if funding continues at some level. This ongoing effort, though daunting in scope and complexity, would greatly magnify the value of numerical climate model outputs and climate observations for future national and international climate-assessment reports. The ESG-CET team also faces substantial technical challenges due to the rapidly increasing scale of climate simulation and observational data, which will grow, for example, from less than 50 terabytes for the last Intergovernmental Panel on Climate Change (IPCC) assessment to multiple Petabytes for the next IPCC assessment. In a world of exponential technological change and rapidly growing sophistication in climate data analysis, an infrastructure such as ESGF must constantly evolve if it is to remain relevant and useful.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Foldy: An open-source web application for interactive protein structure analysis

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

GenomeDepot: data management system for microbial comparative genomics

Summary GenomeDepot is an open-source web-based platform for annotation, management, and comparative analysis of microbial genomic sequences and associated data including ortholog families, protein domains, operons, regulatory interactions, strain taxonomy, and sample metadata. GenomeDepot supports rapid creation of websites for user-defined genome collections that include bioinformatic tools for interactive genome browsing, Basic Local Alignment Search Tool (BLAST) search, annotation search, comparative genomic neighborhood visualization, and sequence download. Gene function annotations are generated by a customizable annotation pipeline. The pipeline runs annotation tools in Conda environments and can be easily extended with additional user-specified tools. Availability and implementation GenomeDepot is open source and distributed under the GNU General Public License via GitHub (https://github.com/aekazakov/genome-depot). GenomeDepot is implemented in Python and was tested in Ubuntu Linux. Full installation instructions and documentation are available at https://aekazakov.github.io/genome-depot/. GenomeDepot demo server is freely accessible at https://iseq.lbl.gov/demogd/.

Kazakov, Alexey [Lawrence Berkeley National Labora↗
DOE OSTI2019

Tracking Restoration And Closure (TRAC): Status Update on Story Maps for Communicating Site Cleanup and Closure Status - 19033

The U.S. Department of Energy (DOE) Office of Environmental Management (EM) initiated the development of a customized, web-based mapping tool for communicating technical information on groundwater plumes at sites within the EM complex. Although the online tool was initially conceived to be a simple conversion of a paper report on contaminant plume sizes, remedial approaches, and regulatory status, it has advanced to providing specific information on cleanup status and site closure. The story maps provide robust geospatial visualization of plumes at DOE sites, as well as explanatory text, photographs, and video for an entire site, or for a management unit within the site. This collection of story maps is called TRAC (Tracking Restoration And Closure), and not only communicates information for each individual site, but also summarizes pertinent metrics on cleanup and remaining contaminants across all EM sites. TRAC is focused on communicating cleanup status, technical challenges, and needs for site closure, with a key challenge of transforming data into relevant, easily understood information. Although DOE managers are the target audience, the story maps can also be used to contribute to public understanding of groundwater cleanup progress at EM sites. Unlike the data access tools maintained at some EM sites (e.g., Hanford, Portsmouth, and Paducah), TRAC is neither a portal for data downloads, nor a tool for performing data analytics. Rather, the goal of TRAC is to provide relevant information on cleanup progress and closure that is consistent across all sites, allowing the user to view summary information across all sites, or examine information relevant to a particular site. This information sharing is needed to maintain open communications with interested parties and communicate efforts that ensure long-term protection of human health and the environment. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

J-GEM optical and near-infrared follow-up of gravitational wave events during LIGO’s and Virgo’s third observing run

The Laser Interferometer Gravitational-wave Observatory Scientific Collaboration and Virgo Collaboration (LVC) sent out 56 gravitational-wave (GW) notices during the third observing run (O3). The Japanese Collaboration for Gravitational wave ElectroMagnetic follow-up (J-GEM) performed optical and near-infrared observations to identify and observe an electromagnetic (EM) counterpart. We constructed a web-based system that enabled us to obtain and share information on candidate host galaxies for the counterpart, and the status of our observations. Candidate host galaxies were selected from the GLADE catalog with a weight based on the 3D GW localization map provided by LVC. We conducted galaxy-targeted and wide-field blind surveys, real-time data analysis, and visual inspection of observed galaxies. We performed galaxy-targeted follow-ups to 23 GW events during O3, and the maximum probability covered by our observations reached 9.8%. Among these, we successfully started observations for 10 GW events within 0.5 days after the detection. This result demonstrates that our follow-up observation has the potential to constrain EM radiation models for a merger of binary neutron stars at a distance of up to ~100 Mpc with a probability area of ≤ 500 deg 2 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

OpenET: Filling a Critical Data Gap in Water Management for the Western United States

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗
DOE OSTI2022

Distributed Energy Resource Cybersecurity Framework and Cyber Range Integration

Distributed energy resource (DER) systems feature complex, data-driven communications networks that require careful system coordination and constant vigilance to ensure that grid assets are secure. Because DERs are an important component of the decarbonization strategy, agencies need to secure energy data that could implicate issues of national security if compromised. To help federal energy managers assess, monitor, and manage cybersecurity while achieving decarbonization, the National Renewable Energy Laboratory's (NREL's) Distributed Energy Resource Cybersecurity Framework (DER-CF) offers a comprehensive, web-based assessment tool focusing on cyber governance or policies, technical management, and physical security. The DER-CF currently presents users with a series of pertinent cybersecurity questions that are used to generate a site-specific report and recommendations. This paper outlines a plan to integrate the DER-CF with another key asset-NREL's cyber range-to visualize cybersecurity resilience and compliance and to enhance the usability and accessibility of the DER-CF for federal facility energy managers and planners. This integration will result in a visualization environment to interpret and interact with compliance data. Its development will include regular conversations with stakeholders to assess the effectiveness of these efforts, refine the visualization capability, and ensure its value to our partners.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Generic Data Display (GD2)

SAND2023-11967O Generic Data Display (GD2) is a real-time data visualization application that can display user-defined input data. The open-source software is comprised of a back end system written in Python, and a front end user interface written in JavaScript. The back end system collects data from a variety of input sources, such as message queue, HTTP, XML, JSON, and others. The front end displays data in an Open MCT web interface, and users can configure the system by providing JSON formatted configuration files. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Figueroa, Benjamin↗
DOE OSTI2020

Drone Video Platform—Collision Avoidance, Situational Awareness, and Communications

Small unmanned aerial systems are ideal for delivering sensors into areas that are inaccessible or too dangerous for human entry or manned aircraft over-flights. The systems can fly low and slow for enhanced sensitivity and in tight spaces where ordinary aircraft could never fit. We have configured and flown sensor payloads for radiation, chemical, and optical detection on small fixed-wing and rotary-wing aircraft. All the sensor packages provide telemetry, GPS coordinates, LIDAR distance ranging, and absolute time of day to microsecond accuracy. Payload weights range from less than 1 kg up to 5.5 kg. Currently, communications are over 2.4 GHz and 915 MHz radios, as well as over 4G LTE cellular data links. Real-time data feeds from the sensor payloads to web browser clients anywhere in the world are possible via a virtual EC2 server on the Amazon AWS cloud. In FY 2019, the Nevada National Security Site teamed with Virginia Tech, H3D Corp., and Unmanned Systems, Inc. to accomplish the four flight missions described in this report. A large part of this year’s mission goals were to demonstrate the ability to fly beyond visual line of sight (BVLOS).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Towards Net Zero: Modeling Approach to the Right-Sized Facilities

As the concentration of greenhouse gases (GHGs) in the atmosphere increases, the concerns about carbon emissions are growing. Several net-zero initiatives are taking place around the globe to achieve a balance between the GHGs put into the atmosphere and those taken out. While most efforts present a sectorized approach, this paper describes the importance of integrating information across different sectors for effective modeling of carbon emissions and holistic reduction opportunity analysis. Using the Idaho National Laboratory (INL) campus as a test case, this work provides a web-based tool for INL stakeholders to use when engaging in strategic planning to achieve carbon emissions reduction. This net-zero engineering support tool (NEST) uses historical data as foundational information for applying the modeling framework. Prediction of CO 2 emissions throughout project completion integrates various approaches and schedules aimed at energy conservation, fleet decarbonization, and other GHG reduction activities. Using NEST, stakeholders can visualize carbon emissions, electricity consumption, and costs for decision making when planning the pathway for reaching carbon net zero. The INL’s initiative to transition into an EV fleet was used to demonstrate the developed framework and the advantages of using NEST. It was shown that electrifying different fossil-fueled campus vehicles before 2030 with aggressive replacement schedules require high annual capital expenditure (CAPEX), which may not be available. The tool allows decision makers to test different replacement schedules and prioritize those that yield CAPEX below a certain threshold while meeting target milestones. While the demonstration focused on vehicle electrification, the developed framework lays the foundation for further quantitative analysis of other GHG reduction activities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Building a framework to genetically characterize “feather spots” and understand demographic impacts of solar energy sites on migratory bird populations

The lack of data on the impact of utility-scale solar facilities on avian species and populations adds to the cost of siting and operation. As much as 32 percent of the avian biological material (feathers and carcasses) recovered from solar facilities remain unidentified, because they often take the form of “feather spots”. Feather spots are remains of impacted animals that can be separated into two broad categories: 1) those remains that may be visually identified to a species, or 2) those that cannot be visually identified to a species due to degradation from the environment and/or scavenger activity (listed as “unknown”). Even when feather spots can be identified to species, they cannot be visually assigned to particular breeding populations. In some cases, it is unknown whether multiple feather spots represent single or multiple individuals. This project’s objectives were to: 1. Use a developed, genetic-based technique to identify and determine the species, population of origin, and number of individuals found in feather spots recovered from solar facilities. 2. Implement collected data and resulting analyses to develop a publicly accessible web-based decision-making tool that can be used by the solar industry, regulators and other stakeholders to inform siting, mitigation, and conservation management efforts. 3. Establish a not-for-profit fee-for-service center at UCLA to ensure collection and identification of feather spots continue after the project period of performance. During the Project Period, we proposed to establish a pipeline for collecting, transporting, and storing of avian biological material collected at solar facilities and the collection and identification of feather spots to species and individual. We proposed the development of a genetic-based framework that would recover viable DNA from feather spots, amplify this DNA (i.e., make millions of copies of the original DNA), and use it to match the resulting sequences to a national database of known species of birds. The result would be the identification of feathers spots that were previously unidentified, and the incorporation of these samples into a larger database that included all samples recovered from solar facilities. The resulting report (below) details the result of this work and its alignment with proposed activities. We proposed the use of the data collected to assess the comparative risk to specific species or populations of species from solar facilities. For some species, we have already identified genomic markers of specific breeding populations and developed “genoscapes,” maps of unique genetic variation across the full breeding range of a species. We used these (previously and newly developed) genoscapes to probabilistically link a feather spot to the specific breeding populations from which it originated (assignment probabilities range from 75%-100% depending on species and population groups). For those species without genoscapes, we developed a vulnerability and susceptibility estimate that determines the relative local and regional risk to populations that are in geographic proximity to solar facilities, using citizen science data (Breeding Bird Survey (BBS) and eBird). These two feather spot processing pipelines (see Figure 1 below) provide quantitative estimates as to the numbers of individuals from a given population of origin that are affected by solar facilities, and ultimately can reduce costs to the consumer by reducing the industry costs associated with mitigation and siting strategies for future solar energy development.

14 SOLAR ENERGY↗
DOE OSTI2019

Multiscale Solar Water Heating (MSWH) v1.0

The main purpose of the software is to model energy use for individual and community scale solar water heating projects in California. Usage The user provides a climate zone for a project, an occupancy for each household and whether any of the occupants stay at home during the day. The software can then load a set of example California specific hourly domestic hot water end-use load profiles from a database, size and locate the systems. The user can now simulate the hourly system performance over a period of one representative year, visualize and explore the simulation results using time-series plots for temperature profiles, heat and power rates, or look at annual summaries. Similarly the user can model individual household solar water heating projects and base case conventional gas tank water heater systems, such that the results can be compared between the individual, community and base case systems. This functionality is readily available through a Jupyter notebook and a Django web framework, depending on what level of detail the user would like to access. Features - Python module to calculate solar irradiation on a tilted surface - Python module with simplified component models for Converter (solar collectors, electric resistance heater, gas burner, photovoltaic panels, heat pump), Storage (solar thermal tank, heat pump thermal tank, conventional gas tank water heater), and Distribution (distribution and solar pump, piping losses) components - Python module with preconfigured system simulation models for: base case gas tank water heaters, solar thermal water heaters (solar collector feeding a storage tank, with a tankeless gas water heater backup in a new installation cases and a base case gas tank water heater in a retrofit case) and solar electric water heaters (heat pump storage tank with an electric resistance backup) - Database with component performance parameters, California specific weather data and domestic hot water end-use load profiles - Django web framework to configure project, parametrize components and run simulation from a web browser The package contains functional and unit tests and it is structured so that it can be extended with further technologies, applications and locations.

Grahovac, Milica↗
DOE OSTI2023

A centralized resource for bacterial–fungal interactions research

Research on bacterial-fungal interactions (BFIs) has revealed that fungi and bacteria frequently interact with one another within diverse ecosystems and microbiomes. Assessing the current state of knowledge within the field of BFI research, particularly with respect to what interactions between bacteria and fungi have been previously described, is very challenging and time consuming. This is largely due to a lack of any centralized resource, with reports of BFIs being spread across publications in numerous journals using non-standardized text to describe the relationships. To address this issue, we have developed the BFI Research Portal, a publicly accessible database of previously reported interactions between bacterial and fungal taxa to serve as a centralized resource for the field. Users can query bacterial or fungal taxa to see what members from the other kingdom have been observed as interaction partners. Search results are accompanied by interactive and intuitive visual outputs, and the database is a dynamic resource that will be updated as new BFIs are reported.

59 BASIC BIOLOGICAL SCIENCES↗